Applied Microeconomics
Applied Microeconomics
The Applied Microeconomics research group unites researchers working on a broad array of topics within such areas as labour economics, economics of education, health economics, family economics, urban economics, environmental economics, and the economics of science and innovation. The group operates in close collaboration with the CAGE Research Centre.
The group participates in the CAGE seminar on Applied Economics, which runs weekly on Tuesdays at 2:15pm. Students and faculty members of the group present their ongoing work in two brown bag seminars, held weekly on Tuesdays and Wednesdays at 1pm. Students, in collaboration with faculty members, also organise a bi-weekly reading group in applied econometrics on Thursdays at 1pm. The group organises numerous events throughout the year, including the Research Away Day and several thematic workshops.
Our activities
Work in Progress seminars
Tuesdays and Wednesdays 1-2pm
Students and faculty members of the group present their work in progress in two brown bag seminars. See below for a detailed scheduled of speakers.
Applied Econometrics reading group
Thursdays (bi-weekly) 1-2pm
Organised by students in collaboration with faculty members. See the Events calendar below for further details
People
Academics
Academics associated with the Applied Microeconomics Group are:
Research Students
Events
CRETA Seminar - James Best (CMU)
Title: Divide and Confer: Aggregating Information without Verification.
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Abstract
We study mechanisms for aggregating information divided across a large population of biased senders. Each sender privately observes an unconditionally independent signal about an unknown state, so no sender鈥檚 report can be verified against another鈥檚. A receiver makes a binary accept/reject decision whose payoffs depend on the state. Even though cross-verification is impossible, we show the receiver can benefit from informational division. We introduce a novel incentive-compatibility-in-the-large approach that studies optimal design via the large-population limit. For fixed population size, optimal mechanisms are in general complex. However, we show that in the limit they converge to a simple mechanism that depends only on the payoff from acceptance, and punishes excessive consensus in the direction of the common bias. These surplus burning punishments yield payoffs bounded away from the first best; the resulting inefficiency demonstrates how our concept of informational division is distinct from standard models of information in large populations.